A Heisenberg-esque Uncertainty Principle for Simultaneous (Machine) Learning and Error Assessment?

TL;DR

This paper proposes a Heisenberg-like uncertainty principle for machine learning, suggesting that optimizing learning and error assessment simultaneously using the same data is fundamentally limited, and that some information should be reserved for error evaluation.

Contribution

It establishes a Cramer-Rao-style lower bound linking learning regret and error assessment correlation, highlighting a fundamental trade-off in data usage.

Findings

Lower bound on regret based on error assessment correlation

Theoretical connection between learning and error evaluation constraints

Implication that reserving information for error assessment can improve reliability

Abstract

A highly cited and inspiring article by Bates et al (2024) demonstrates that the prediction errors estimated through cross-validation, Bootstrap or Mallow's $C_P$ can all be independent of the actual prediction errors. This essay hypothesizes that these occurrences signify a broader, Heisenberg-like uncertainty principle for learning: optimizing learning and assessing actual errors using the same data are fundamentally at odds. Only suboptimal learning preserves untapped information for actual error assessments, and vice versa, reinforcing the `no free lunch' principle. To substantiate this intuition, a Cramer-Rao-style lower bound is established under the squared loss, which shows that the relative regret in learning is bounded below by the square of the correlation between any unbiased error assessor and the actual learning error. Readers are invited to explore generalizations, develop variations, or even uncover genuine `free lunches.' The connection with the Heisenberg uncertainty principle is more than metaphorical, because both share an essence of the Cramer-Rao inequality: marginal variations cannot manifest individually to arbitrary degrees when their underlying co-variation is constrained, whether the co-variation is about individual states or their generating mechanisms, as in the quantum realm. A practical takeaway of such a learning principle is that it may be prudent to reserve some information specifically for error assessment rather than pursue full optimization in learning, particularly when intentional randomness is introduced to mitigate overfitting.